Method of Resource Estimation Based on QoS in Edge Computing
With the development of Internet of Things, the number of network devices is increasing, and the cloud data center load increases; some delay-sensitive services cannot be responded to timely, which results in a decreased quality of service (QoS). In this paper, we propose a method of resource estima...
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Series: | Wireless Communications and Mobile Computing |
Online Access: | http://dx.doi.org/10.1155/2018/7308913 |
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doaj-84f4cc42d3aa49bc81f2cffc858616562020-11-25T01:05:57ZengHindawi-WileyWireless Communications and Mobile Computing1530-86691530-86772018-01-01201810.1155/2018/73089137308913Method of Resource Estimation Based on QoS in Edge ComputingGuangshun Li0Jianrong Song1Junhua Wu2Jiping Wang3School of Information Science and Engineering, Qufu Normal University, Rizhao 276800, ChinaSchool of Information Science and Engineering, Qufu Normal University, Rizhao 276800, ChinaSchool of Information Science and Engineering, Qufu Normal University, Rizhao 276800, ChinaSchool of Information Science and Engineering, Qufu Normal University, Rizhao 276800, ChinaWith the development of Internet of Things, the number of network devices is increasing, and the cloud data center load increases; some delay-sensitive services cannot be responded to timely, which results in a decreased quality of service (QoS). In this paper, we propose a method of resource estimation based on QoS in edge computing to solve this problem. Firstly, the resources are classified and matched according to the weighted Euclidean distance similarity. The penalty factor and Grey incidence matrix are introduced to correct the similarity matching function. Then, we use regression-Markov chain prediction method to analyze the change of the load state of the candidate resources and select the suitable resource. Finally, we analyze the precision and recall of the matching method through simulation experiment, validate the effectiveness of the matching method, and prove that regression-Markov chain prediction method can improve the prediction accuracy.http://dx.doi.org/10.1155/2018/7308913 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Guangshun Li Jianrong Song Junhua Wu Jiping Wang |
spellingShingle |
Guangshun Li Jianrong Song Junhua Wu Jiping Wang Method of Resource Estimation Based on QoS in Edge Computing Wireless Communications and Mobile Computing |
author_facet |
Guangshun Li Jianrong Song Junhua Wu Jiping Wang |
author_sort |
Guangshun Li |
title |
Method of Resource Estimation Based on QoS in Edge Computing |
title_short |
Method of Resource Estimation Based on QoS in Edge Computing |
title_full |
Method of Resource Estimation Based on QoS in Edge Computing |
title_fullStr |
Method of Resource Estimation Based on QoS in Edge Computing |
title_full_unstemmed |
Method of Resource Estimation Based on QoS in Edge Computing |
title_sort |
method of resource estimation based on qos in edge computing |
publisher |
Hindawi-Wiley |
series |
Wireless Communications and Mobile Computing |
issn |
1530-8669 1530-8677 |
publishDate |
2018-01-01 |
description |
With the development of Internet of Things, the number of network devices is increasing, and the cloud data center load increases; some delay-sensitive services cannot be responded to timely, which results in a decreased quality of service (QoS). In this paper, we propose a method of resource estimation based on QoS in edge computing to solve this problem. Firstly, the resources are classified and matched according to the weighted Euclidean distance similarity. The penalty factor and Grey incidence matrix are introduced to correct the similarity matching function. Then, we use regression-Markov chain prediction method to analyze the change of the load state of the candidate resources and select the suitable resource. Finally, we analyze the precision and recall of the matching method through simulation experiment, validate the effectiveness of the matching method, and prove that regression-Markov chain prediction method can improve the prediction accuracy. |
url |
http://dx.doi.org/10.1155/2018/7308913 |
work_keys_str_mv |
AT guangshunli methodofresourceestimationbasedonqosinedgecomputing AT jianrongsong methodofresourceestimationbasedonqosinedgecomputing AT junhuawu methodofresourceestimationbasedonqosinedgecomputing AT jipingwang methodofresourceestimationbasedonqosinedgecomputing |
_version_ |
1725192253661511680 |